The present invention generally relates to remote inspection techniques. The invention particularly relates to automated remote inspection for detection of cracks in a surface.
It is generally accepted that in the absence of adequate periodic inspection and follow-up maintenance, civil infrastructure systems, including nuclear power plant components, inevitably deteriorate, in large part due to excessive long-term usage, overloading, and aging materials. In particular, regular inspection of nuclear power plant components, for example, for cracks, is an important task to improve their resiliency. Nuclear power plant reactors are typically submerged in water. Consequently, direct manual inspection of reactors is unfeasible due to high temperatures and radiation hazards. An alternative solution is to use a robotic arm to remotely record videos at the underwater reactor surface.
However, inspections that rely on remote visual techniques, wherein an inspector reviews optical images or video of the components, can be both time-consuming and subjective. Recent blind testing of remote visual examination personnel and techniques has identified a need for increased reliability associated with identifying cracks from review of live and recorded data. Results indicate that reliable crack identification can be degraded by human performance even when identification should be evident. The quantity and complexity of reviewing large quantities of data increase the likelihood of human error.
The utilization of automated crack detection algorithms can improve the speed of the exams and reduce the potential for human error. Most existing automatic crack detection algorithms are based on edge detection, thresholding, or morphological operations. However, these types of automated crack detection algorithms may fail to detect cracks on metallic surfaces since these cracks are typically very small and have low contrast. In addition, the existence of various “non-crack” surface texture features, for example, surface scratches, welds, and grind marks, may lead to a large number of false positives, that is, mistakenly attributing a non-crack surface texture feature to be a crack on a surface, especially if the non-crack surface texture features have relatively linear shapes and stronger contrast than the cracks.
In view of the above, it can be appreciated that there is an ongoing desire for improved inspection methods and systems capable of reliably detecting surface cracks, for example, during inspections of nuclear power plant components.